arXiv subjects

Allen Sun

Publications and source records attributed to Allen Sun.

3 recordsLinked to original sources

arXiv2026

AdNanny: One Reasoning LLM for All Offline Ads Recommendation Tasks

Large Language Models (LLMs) have shown strong capabilities in Natural Language Understanding and Generation, but deploying them directly in online advertising systems is often impractical due to strict millisecond-level latency constraints. This has motivated the use of LLMs offline to improve retrieval, ranking, and recommendation models. Existing solutions typically fine-tune separate LLMs for individual tasks such as query-ad relevance labeling, keyword-based query generation, and user profiling. This results in redundant models, high maintenance cost, and limited performance gains despite substantial overlap in domain knowledge and reasoning patterns. We introduce AdNanny, a unified reasoning-centric LLM that serves as a shared backbone for offline advertising tasks. AdNanny is obtained by fine-tuning a public 671B-parameter DeepSeek-R1 checkpoint using a scalable training system that supports hybrid dense-MoE parallelism. We construct reasoning-augmented corpora that pair structured supervision with step-by-step natural language explanations. A multi-task supervised fine-tuning stage with adaptive reweighting enables AdNanny to handle diverse labeling and generation tasks in a consistent reasoning format. This is followed by reinforcement learning using downstream advertising metrics to align model behavior with online retrieval and ranking objectives. AdNanny is deployed in production within Bing Ads, where it significantly reduces manual labeling effort and improves accuracy across multiple offline tasks. By consolidating many task-specific models into a single reasoning-centric foundation model, AdNanny provides a scalable and cost-effective solution for large-scale advertising systems.

cs.SE↗

arXiv2025

Shape-controlled growth of two-dimensional kagome-lattice colloidal crystals through nanoparticle capping

Organic capping ligands can selectively bind to crystal facets to modulate growth kinetics and are important in chemical synthesis of inorganic nanocrystals. Using the capping ligands for shape-controlled growth of colloidal crystals is challenging due to the size mismatch of molecules and nanoparticle building blocks. In existing synthetic pathways, colloidal crystal shapes are determined by their thermodynamically favored phases yet controlling their shapes independent of lattice symmetry is vital to study many solid-state properties. Here, we develop a nanoparticle capping strategy to control colloidal crystal shapes and structural heterogeneity. Au bipyramids were used as building blocks and assembled into rhombohedral colloidal crystals driven by DNA hybridization. In (111) planes of the crystals, bipyramids assembled into kagome lattices, featuring structure cavities organized in a hexagonal lattice. The rhombohedral crystals have truncated tetrahedral crystal habits, and the degree of truncation defines the exposed facets and crystal shapes. Our surface capping strategy is to introduce DNA-modified nanospheres as effective capping agents, which selectively register on the surface vacancies of the kagome facets and resemble the role of organic ligands in classic nanocrystal growth. Such selective capping is driven by maximizing DNA hybridization and leads to slower growth of the (111) kagome facets, changing the crystal shape from three-dimensional truncated tetrahedra to two-dimensional layered microplates with structural heterogeneity and shape anisotropy. This study underpins the importance of capping agents in colloidal crystal growth and inspires effective ways to control the growth kinetics and heterostructures of colloidal crystals.

cond-mat.soft↗

arXiv2024

Unleash LLMs Potential for Recommendation by Coordinating Twin-Tower Dynamic Semantic Token Generator

Owing to the unprecedented capability in semantic understanding and logical reasoning, the pre-trained large language models (LLMs) have shown fantastic potential in developing the next-generation recommender systems (RSs). However, the static index paradigm adopted by current methods greatly restricts the utilization of LLMs capacity for recommendation, leading to not only the insufficient alignment between semantic and collaborative knowledge, but also the neglect of high-order user-item interaction patterns. In this paper, we propose Twin-Tower Dynamic Semantic Recommender (TTDS), the first generative RS which adopts dynamic semantic index paradigm, targeting at resolving the above problems simultaneously. To be more specific, we for the first time contrive a dynamic knowledge fusion framework which integrates a twin-tower semantic token generator into the LLM-based recommender, hierarchically allocating meaningful semantic index for items and users, and accordingly predicting the semantic index of target item. Furthermore, a dual-modality variational auto-encoder is proposed to facilitate multi-grained alignment between semantic and collaborative knowledge. Eventually, a series of novel tuning tasks specially customized for capturing high-order user-item interaction patterns are proposed to take advantages of user historical behavior. Extensive experiments across three public datasets demonstrate the superiority of the proposed methodology in developing LLM-based generative RSs. The proposed TTDS recommender achieves an average improvement of 19.41% in Hit-Rate and 20.84% in NDCG metric, compared with the leading baseline methods.

cs.IR↗